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A Versatile Framework for Data-Driven Control of Nonlinear Systems
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This note aims to provide a systematic investigation of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a unifying, versatile, and broad framework that enables the generation of novel results in this domain. We formulate the nonlinear design problem from a high-level perspective as a set of desired controlled systems and propose systematic procedures to synthesize data-driven control algorithms that meet the specified design requirements. Various examples are presented to demonstrate the applicability of the proposed approach and its ability to derive new insights and results, illustrating the novel contributions enabled by the framework.
Forward citations
Cited by 2 Pith papers
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Data-Efficient Control of Polynomial Systems via Physics-Guided Quadratic Constraints
A physics-guided quadratic constraint added to a sum-of-squares optimization lets a single short noisy trajectory certify robust safety for polynomial control systems, reducing data needs substantially.
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Data-Driven Stochastic Control: Foundations and Guarantees
A data-driven framework synthesizes stochastic control barrier certificates and safety controllers for unknown polynomial stochastic systems, with probabilistic safety guarantees and certified confidence from multiple...
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